Methods for Detecting Pitch in Lumber

ABSTRACT

Methods are provided for detecting compression wood, blue stain, or pitch in lumber. A light beam is projected towards the wood sample. Line or area cameras acquire images of light that is reflected from the wood sample. Based on the intensity of the reflected light at one or more locations on the wood sample, compression wood, blue stain, or pitch may be detected.

FIELD OF THE INVENTION

This invention relates generally to methods for detecting pitch inlumber.

BACKGROUND OF THE INVENTION

It is generally known to identify compression wood, blue stain, andpitch in a wood sample. With respect to detecting compression wood,known methods would include optical and scanning electron microscopy toidentify compression wood areas. In normal wood, the S2 layer (thethickest cell wall layer comprised of ordered microfibrils nearlyparallel to the long axis of the cell) is continuous and characterizedby low microfibril angles. In compression wood the S2 layer is fracturedand is characterized by high microfibril angles. Microscopy methods havethe disadvantage that they cannot be applied in real time, in anindustrial setting with lumber moving at planar speeds (up to 2500 fpm).The other primary method used to identify compression wood consists oftransmission imaging of thin cross sections of boards. In this method,thin cross sections are imaged in transmission mode either in a scanneror photographic setup. Areas of compression wood appear as relativelyopaque areas. The severity of compression wood can be estimated bymapping the variation in opacity in these areas. More severe compressionwood transmits less light than less severe areas. The thin sectiontransmission method, however, is also not appropriate for a real-timeindustrial application for obvious reasons. In addition, methods fordetection of blue stain and pitch have demonstrated shortcomings.

Accordingly, a need exists for methods for more efficient detection ofcompression wood, blue stain, or pitch in lumber.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the present invention are described in detail belowwith reference to the following drawings.

FIG. 1 is an illustration of compression wood detection steps;

FIG. 2 is an illustration of the correlation of this compression woodmethod with the method using light transmission in thin slices;

FIG. 3 is an illustration of blue stain on southern pine, its effect onthe tracheid image and detection using the HSI method;

FIG. 4 is an illustration of the appearance of pitch, its effect on thetracheid effect, and it's detection by thresholding the tracheid image;

FIG. 5 is a diagram of a detection system implementing line cameras;

FIG. 6 is a diagram of a detection system implementing an area camera;

FIG. 7 is a diagram of a detection system implementing an area camera;

FIG. 8 is a diagram of a detection system implementing an area RGBcamera; and

FIG. 9 is a diagram of a detection system implementing multiple line RGBcameras.

DETAILED DESCRIPTION OF THE INVENTION

The present invention generally relates to detection of compressionwood, blue stain, or pitch in a wood sample. A light beam is projectedtowards the wood sample. The light beam may be in the form of a laserline. In an embodiment, the light beam may be in the form of individualspots of light. Line or area cameras acquire images of light that isreflected from the wood sample. Based on the intensity of the reflectedlight at one or more locations on the wood sample, compression wood,blue stain, or pitch may be detected.

Compression Wood Detection in Lumber

In an embodiment, the compression wood detection system is directed totransporting lumber longitudinally past an image acquisition system. Theimaging system consists of laser lines projected across the width of theboard faces (top and bottom) and either line cameras or area cameras torecord the intensity of diffusely reflected light on either side of thelaser line. The method can be extended to include the board edges aswell with additional or modified hardware.

The laser line may be of sufficient intensity to saturate or nearlysaturate the cameras at integration times which are of a durationallowing for minimal board movement during the integration period, suchas, for example, less than 0.1 inch at 2000 feet per minute board speed.The laser wavelength may be red or near infra-red, such as for example,680-850 nm.

The line camera imaging system consists of two or more line cameras (oneset for each board face) where the lines are aimed parallel to and atfixed distances from the center of the laser line; one line camera beingaimed closer to the laser line and the second being aimed further fromthe center. In this way, the two lines measure the intensity drop of thediffuse reflection of the laser, which is representative of the T1 or‘tracheid’ effect, known by those skilled in the art.

If area cameras are being used, multiple laser lines can be projected inthe field of view, and a single frame capture can be used to image alarger area of the board (e.g. full width and 12 inches along thelength). In this case, laser lines should be spaced so that they areseparated by dark areas.

In this way, the decay or fall off of the diffusely reflected laserlight (T1 effect) can be measured on wood (illustrated in FIG. 1 for theline camera example).

Areas of compression wood contain cells with high microfibril angles inthe S2 layer of the cell wall. The high microfibril angle of compressionwood blocks the transmission of the laser light along the axis of thecell. In contrast, normal latewood contains cells with relatively lowermicrofibril angles in the S2 layer which transmits laser light moreeffectively. Therefore, the diffuse reflection intensity in compressionwood areas decreases more rapidly than in areas of normal wood. As aresult, the intensity along the first line camera (as in the exampleillustrated in FIG. 1) will be lower in compression wood than in normallatewood. Therefore, the difference in intensity between the first lineand a more remote line will be lower in areas of compression woodcompared with normal late wood. The intensity difference between the twoline cameras (or pixel row if using an area camera) is representative ofthe slope of the decay in intensity and is a more robust measurement incompression wood identification than intensity alone. In a sense, usingthe slope or difference of intensity helps normalize the measurement forcolor differences in wood. In addition to the cameras, the imagingsystem may require processing software to perform image analysis steps.

For an embodiment in which a line camera is used, the method may havethe steps of acquiring successive simultaneous images from the set ofline cameras for the entire length of the board; reassemblingconsecutive scans to create an image of the board from each of the linecameras; using a ‘perimeter’ image (acquired from separate geometricscanning system) to ‘straighten’ the board to remove any effects ofsniping through the scanner; using a ‘wane perimeter’ image (acquiredfrom a separate geometric scanning system) to locate any wane areas anduse this information to create a wane mask. Wane area affects thereflection intensity of the laser line and is not processed further. Theuse of a geometric scanning system assures that only those parts of theboard surface which can be properly imaged are used in the detection ofcompression wood.

In additional steps, knots, blue stain and pitch are identified usingcolor (RGB) images (acquired from separate color scanning system) andthese areas are masked from processing for compression wood. Knots andblue stain attenuate the diffuse reflection of the laser line andinterfere with a compression wood algorithm. Pitch intensifies thediffuse reflection of the laser line and also interferes with thecompression wood algorithm.

In further steps, the more remote tracheid image (that furthest from thecenter of the laser line) is subtracted from the nearest tracheid image(that closest to the center of the laser line), to create a ‘difference’image. Note that areas masked for wane, knots, blue stain andcompression wood should not be processed or should be represented byzero intensity.

Next, the ‘difference’ image is thresholded between two grey scaleintensities. The lower and upper threshold limits will be dependent onthe camera and laser setup and may need to be adjusted for each system.The upper and lower threshold limits are set by manually identifyingcompression wood areas on imaged boards either by wood scientists or bythin section transmission measurements. Thresholds are then set so thatthe image area between the upper and lower thresholds match that of themanually identified compression wood. Following thresholding, small‘particles’ of compression wood are removed from the thresholdeddifference image. Small ‘holes’ in compression wood areas are filled infrom the thresholded difference image.

A visual example of the technique is shown in FIG. 2. For this example,the lower intensity threshold was set at 12 grey scale value (0-255);the upper intensity threshold was set at 35 grey scale value.

In addition to the method steps mentioned above, the method could alsoinclude several modifications. These would include using additionalcolor information to limit the amount of area identified as compressionwood. As is evident in the example described above and illustrated inFIG. 2, compression wood areas tend to appear as wider areas of latewoodin a tracheid effect image, compared to that in normal wood. By using acolor mask in addition to the thresholded difference image, a morerobust measure of compression wood may be possible. One way to make acolor mask for compression wood application would be to convert RGBimages to HSI space (hue, saturation, intensity) and to limitcompression wood areas to those containing certain hues (primarily), andintensities. This variation in the method will probably need to beadjusted for each species and perhaps even geographies.

There is good correlation between the thin-section transmission methodand the identification of compression wood by the intensity differencemethod outlined above. Shown in FIG. 2 is an example of thiscorrelation. Top and bottom faces from RGB and laser line cameras areshown together with transmission light images from corresponding slicesfrom the end of the same board section. Notice that the method describedhere accurately identifies the location of compression wood bandsobvious in the transmission images of the end slices.

In an embodiment, a method is provided for detecting compression wood ina wood sample. A system for practicing the method is illustrated in FIG.5. The method has the steps of: projecting a coherent light beam towarda first section of the wood sample; acquiring a first image of reflectedlight using a first line camera; measuring a first intensity of thereflected light based on the first image; acquiring a second image ofreflected light using a second line camera; measuring a second intensityof the reflected light based on the second image; measuring a differencebetween the first intensity and the second intensity; and detecting anarea of compression wood in the wood sample wherein the detection isbased on whether the difference between the first intensity and thesecond intensity is within a predetermined intensity range.

In another embodiment, a method is provided for detecting compressionwood in a wood sample. A system for practicing the method is illustratedin FIG. 7. The method has the steps of: projecting one or more coherentlight beams toward a first section of the wood sample; acquiring animage of the first section using an area camera; measuring a firstintensity of reflected light along a first pixel row based on the image;measuring a second intensity of reflected light along a second pixel rowbased on the image; measuring a difference between the first intensityand the second intensity; and detecting an area of compression wood inthe wood sample wherein the detection is based on whether the differencebetween the first intensity and the second intensity is within apredetermined intensity range.

In another embodiment, a method is provided for detecting compressionwood in a wood sample. A system for practicing the method is illustratedin FIG. 6. The method has the steps of: projecting one or more coherentlight beams toward a first section of the wood sample; acquiring animage of the first section using an area camera; predetermining a firstintensity; determining a first distance from the light beam at which ameasured reflected intensity is equal to the predetermined firstintensity; predetermining a second intensity; determining a seconddistance from the light beam at which a measured reflected intensity isequal to the predetermined second intensity; measuring a differencebetween the first distance and the second distance; and detecting anarea of compression wood in the wood sample wherein the detection isbased on whether the difference between the first distance and thesecond distance is within a predetermined distance range.

Blue Stain Detection in Lumber

The blue stain detection system consists of a method for transportinglumber longitudinally past an image acquisition system. The imagingsystem consists of a full spectrum light source and either an area RGBcamera per face, such as that illustrated in FIG. 8, or three linecameras per face (one each for red, green, and blue), such as thatillustrated in FIG. 9. The method can be extended to include the boardedges as well with additional or modified hardware.

The full spectrum light source should be of sufficient intensity tosaturate or nearly saturate the cameras at integration times which areof a duration allowing for minimal board movement during the integrationperiod (<0.1″ at 2000 fpm board speed). In addition to the cameras, theimaging system may require processing software to perform image analysissteps.

For an embodiment in which line cameras are used, the method may havethe steps of acquiring successive simultaneous images from each of theline cameras for the entire board; reassembling consecutive scans tocreate an image of the board from each of the line cameras (red, greenand blue channels); using a ‘perimeter’ image (acquired from separategeometric scanning system) to ‘straighten’ the board to remove anyeffects of sniping through the scanner; using a ‘wane perimeter’ image(acquired from separate geometric scanning system) to locate any waneareas and use this to create a wane mask. Wane area affects thereflection intensity of the laser line and is not processed further.

In an additional step, the individual color channels are low passfiltered. This will prevent creation of color artifacts created bycombining data from cameras which are not perfectly registered. Variousfilter types can be used, such as, for example, a 3×3 convolutionfilter. Note that this step may not be required for systems using areacameras.

The data is then combined from all three colors and converted to HSI(Hue, Saturation, Intensity) color space. In a next step, each HSIchannel is thresholded at two levels. In an alternate embodiment, onlythe hue channel can be thresholded. The thresholded HSI images arecombined to produce the blue stain image. In some cases, it may beuseful to further filter the blue stain image map with, for example, a3×3 automedian filter. The thresholds are for hue (and, subsequentlysaturation and intensity) are set by manually identifying blue stainareas on one or many boards, and adjusting the upper and lower huethreshold such that only those areas manually identified on the boardare maintained in the image.

A visual example of this technique is shown in FIG. 3. For this example,the hue threshold limits were 34-106; the saturation threshold limitswere 2-60 and the intensity threshold limits were 2-106.

In an embodiment, a method is provided for detecting blue stain in awood sample. A system for practicing the method is illustrated in FIG.9. The method has the steps of: projecting a full spectrum light beamtoward a first section of the wood sample; acquiring a first image ofreflected light using a first line camera; measuring an intensity ofreflected light in a red region of the full spectrum based on the firstimage; acquiring a second image of reflected light using a second linecamera; measuring an intensity of reflected light in a green region ofthe full spectrum based on the second image; acquiring a third image ofreflected light using a third line camera; measuring an intensity ofreflected tight in a blue region of the full spectrum based on the thirdimage; subjecting the intensity measurements of reflected light in thered, green and blue regions to an algorithm to provide hue, saturationand intensity data; and detecting blue stain in the wood sample based onwhether the hue data is within a pre-selected hue range.

In an embodiment, a method is provided for detecting blue stain in awood sample. A system for practicing the method is illustrated in FIG.8. The method has the steps of: projecting a full spectrum light beamtoward a first section of the wood sample; acquiring a first image ofreflected light using an area camera; measuring an intensity ofreflected light in a red region of the full spectrum based on the firstimage; measuring an intensity of reflected light in a green region ofthe full spectrum based on the first image; measuring an intensity ofreflected light in a blue region of the full spectrum based on the firstimage; subjecting the intensity measurements of reflected light in thered, green and blue regions to an algorithm to provide hue, saturationand intensity data; and detecting blue stain in the wood sample based onwhether the hue data is within a pie-selected hue range.

There are several descriptions of blue stain detection in wood in theprior art. Most involve area cameras and some thresholding in RGB colorspace. The method outlined here may be more beneficial in several ways.First, it involves a processing step to allow the use of line camerasfor color information from separate channels in order to reduce theintroduction of color artifacts created by slight mis-registrations ofthose cameras. The convolution filter used averages the colorinformation across several pixels with appropriate weighting andvirtually eliminates color artifacts.

Secondly, the method is unique in that it converts the color informationfrom RGB space to HSI space. While blue stain detection is possible inthe RGB color space, conversion to HSI space allows the hue channel tobe used as the primary detection channel since the blue stain hue isfairly consistent and different from normal unstained wood. It is easierto set the threshold levels with the single hue channel than trying toset all three RGB channels. After hue range is selected, the saturationand intensity channels can be used to refine the mask. Interestingly,using the blue channel of an RGB image is not an effective way to detectblue stain; all three colors may need to be utilized.

Finally, the application of a blue stain mask to image processing oftracheid effect information is not taught in the prior art. As shown inFIG. 3, blue stain dramatically affects the diffuse reflection intensityused to measure the tracheid effect and may interfere with, amongothers, the detection of compression wood.

Pitch Detection in Lumber

Pitch in wood has the effect of transmitting laser light to a greaterextent along the fiber axis than normal wood (the tracheid effect) andmay interfere with detection of wood features which rely on the tracheideffect (e.g., compression wood). Identification of pitch in wood relieson a system which measures the intensity of diffusely reflected laserlight at a distance from a laser light source using line cameras or areacameras, such as the systems illustrated in FIGS. 5, 6 and 7. Pitch isidentified in areas where the intensity exceeds an intensity thresholdat a certain distance from the laser line (line camera location or areacamera pixel row). The intensity threshold is system dependent and isaffected by the laser intensity, distance between the laser line centerand the line camera focus, integration time of the camera and overallreflectivity of the wood. The intensity threshold may be set by manuallyidentifying areas of pitch and adjusting the threshold until the areaexceeding the threshold intensity matches the manually identified areasof pitch.

Alternately, using area cameras, pitch could be detected as areas wherethe intensity persists above a certain level for greater than a givendistance from the laser light.

In an embodiment, the pitch detection system consists of a method fortransporting lumber longitudinally past an image acquisition system. Theimaging system consists of laser line(s) projected across the width ofthe board faces (top and bottom) and either line cameras or area camerasto record the intensity of diffusely reflected light from the laserline(s). The method can be extended to include the board edges as wellwith additional or modified hardware.

The laser line(s) should be of sufficient intensity to saturate ornearly saturate the cameras (e.g. ˜255 grey scale level for an 8-bitcamera) at integration times which are of a duration allowing forminimal board movement during the integration period (<0.1″ at 2000 fpmboard speed). The laser wavelength may be red or near infra-red (680-850nm).

The line camera imaging system consists of a line camera aimed parallelto and at a fixed distance from the center of the laser line. In thisway, the line measures the intensity and intensity drop of the diffusereflection of the laser, which is representative of the T1 or ‘tracheid’effect. Areas where the line camera records an intensity above athreshold value are identified as pitch containing wood.

If area cameras are being used, one or multiple laser lines can beprojected in the field of view, and a single frame capture can be usedto image a larger area of the board (e.g. full width and 12″ along thelength). In this case, laser lines should be spaced so that they areseparated by dark areas. If the area camera field of view is preciselyaligned to the laser line, the reflected intensity can be measured atvarious distances from the center of a laser line by selectingindividual pixel rows. Areas exceeding the intensity threshold areidentified as pitch.

Alternately, an intensity value can be selected and the distance fromthe center of the laser line to the point where the diffusely reflectedintensity drops below this level can be measured. In this case, adistance threshold can be set to identify pitch. Areas where thedistance from the laser line to the point where the intensity dropsbelow a chosen value exceeds a threshold distance are identified aspitch.

In addition to the cameras, the imaging system requires processingsoftware to perform image analysis steps. Such software is known bythose skilled in the art. In an embodiment, the method has the steps of:acquiring images from line cameras for the entire board; reassemblingconsecutive scans to create an image of the board from each of the linecameras; using a ‘perimeter’ image (acquired from a separate geometricscanning system) to ‘straighten’ the board to remove any effects ofsniping through the scanner.

Use of a ‘wane perimeter’ image (acquired from a separate geometricscanning system) enables location of wane areas and in order to create awane mask. Wane area affects the reflection intensity of the laser lineand may not be not processed further. The tracheid effect image is thenthresholded using a single threshold level. The threshold limit will bedependent on the camera and laser setup and needs to be adjusted foreach system. Small ‘holes’ in pitch containing areas are filled in tocreate a more continuous mask.

A visual example of the technique is shown in FIG. 4. For this example,the pitch threshold was set at 180 grey scale level (on a scale of0-255).

In a n embodiment, a method is provided for detecting pitch in a woodsample. The method has the steps of: projecting a coherent light beamtoward a first section of the wood sample; acquiring a first image ofreflected light using a first line camera focused at a predetermineddistance from the center of the light beam; measuring a first intensityof the reflected light based on the first image; and detecting pitchwithin the wood sample based on whether the first intensity is greaterthan a threshold intensity.

In another embodiment, a method is provided for detecting pitch in awood sample. The method has the steps of: projecting one or morecoherent light beams toward a first section of the wood sample;acquiring an image of the first section using an area camera; measuringa first intensity of reflected light along a first pixel row based onthe image wherein the first pixel row is based on a predetermineddistance from the light beam; and detecting pitch within the wood samplebased on whether the first intensity is greater than a thresholdintensity.

In another embodiment, a method is provided for detecting pitch in awood sample. The method has the steps of: projecting one or morecoherent light beams toward a first section of the wood sample;acquiring an image of the first section using an area camera;predetermining a first intensity; determining a first distance from thelight beam at which an intensity of reflected light is equal to thepredetermined first intensity; and detecting pitch within the woodsample based on whether the first distance is greater than a thresholddistance.

The present invention has advantages over the prior art in that atracheid effect image system can be used to identify pitch pockets. Mostpitch detection systems which rely on imaging systems use RGB colorimages for identification of pitch areas. The appearance of pitch in atracheid effect (diffuse reflected laser light) image is a dramatic andreliable method. The image processing required for pitch detection froma tracheid effect image is a simple, one level thresholding and is,therefore, accomplished in real time. In addition, the use of a pitchmask to exclude areas in other defect detection methods (e.g.compression wood) may prove beneficial.

While the embodiments of the invention have been illustrated anddescribed, as noted above, many changes can be made without departingfrom the spirit and scope of e invention. Accordingly, the scope of theinvention is not limited by the disclosure of the embodiments. Instead,the invention should be determined entirely by reference to the claimsthat follow.

1. A method for detecting pitch in a wood sample, the method comprisingthe steps of: projecting a coherent light beam toward a first section ofthe wood sample; acquiring a first image of reflected light using afirst line camera focused at a predetermined distance from a center ofthe light beam; measuring a first intensity of the reflected light basedon the first image; and detecting pitch within the wood sample based onwhether the first intensity is greater than a threshold intensity. 2.The method of claim 1 wherein the coherent light beam is a laser.
 3. Themethod of claim 1 wherein the light beam creates a line of light acrossthe first section.
 4. The method of claim 3 wherein the line of light isin the form of individual spots of light.
 5. The method of claim 1wherein the light beam has a frequency in a range from 680 nm to 850 nm.6. A method for detecting compression wood in a wood sample, the methodcomprising the steps of: projecting one or more coherent light beamstoward a first section of the wood sample; acquiring an image of thefirst section using an area camera; measuring a first intensity ofreflected light along a first pixel row based on the image wherein thefirst pixel row is based on a predetermined distance from the lightbeam; and detecting pitch within the wood sample based on whether thefirst intensity is greater than a threshold intensity.
 7. The method ofclaim 6 wherein the coherent light beam is a laser.
 8. A method fordetecting compression wood in a wood sample, the method comprising thesteps of: projecting one or more coherent light beams toward a firstsection of the wood sample; acquiring an image of the first sectionusing an area camera; predetermining a first intensity; determining afirst distance from the light beam at which an intensity of reflectedlight is equal to the predetermined first intensity; and detecting pitchwithin the wood sample based on whether the first distance is greaterthan a threshold distance.
 9. The method of claim 8 wherein the lightbeam is a laser.
 10. The method of claim 8 wherein the light beamcreates a line of light across the first section.
 11. The method ofclaim 8 wherein the line of light is in the form of individual spots oflight.